他们要求我做，我做了，但没人用过。

Q: 项目背景

详细描述项目背景和用户需求。

Q: 项目实施

概述项目实施过程中的挑战和解决方案。

Q: 项目结果

总结项目的结果和未被使用的原因。

Q: 结论

反思项目失败的原因并提出改进措施。

Towards Data Science

Towards Data Science2026年5月27日

他们要求我做，我做了，但没人用过。

2.0Score

TL;DR · AI 摘要

这篇文章讲述了作者根据用户请求构建了一个项目，但最终未被使用。

核心要点

项目未被使用的原因可能是需求不明确或实现不符合预期。
作者通过分析用户请求来决定开发方向，但未能成功。
项目的失败可能反映了市场对特定功能的需求不足。

结构提纲

按章节快速跳转。

§引言
介绍作者根据用户请求构建了一个项目，但最终未被使用。
·项目背景
详细描述项目背景和用户需求。
·项目实施
概述项目实施过程中的挑战和解决方案。
·项目结果
总结项目的结果和未被使用的原因。
§结论
反思项目失败的原因并提出改进措施。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）

项目未被使用
- 需求不明确
- 实现不符合预期
- 市场需求不足

金句 / Highlights

值得收藏与分享的关键句。

项目未被使用的原因可能是需求不明确或实现不符合预期。
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X
作者通过分析用户请求来决定开发方向，但未能成功。
— 第 3 段
⬇︎ 下载 PNG 𝕏 分享到 X
项目的失败可能反映了市场对特定功能的需求不足。
— 第 4 段
⬇︎ 下载 PNG 𝕏 分享到 X

#项目管理#需求分析#用户体验

打开原文

They Requested It. I Built It. Nobody Ever Used It. | Towards Data Science

We value your privacy

We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies.

Customise Reject All Accept All

Customise Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ...Show more

Necessary Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

Cookie BCTempID
Duration 10 minutes
Description No description available.

Cookie __cf_bm
Duration 1 hour
Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management.

Cookie AWSALBCORS
Duration 7 days
Description Amazon Web Services set this cookie for load balancing.

Cookie _cfuvid
Duration session
Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services

Cookie li_gc
Duration 6 months
Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.

Cookie __hssrc
Duration session
Description This cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.

Cookie __hssc
Duration 1 hour
Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.

Cookie wpEmojiSettingsSupports
Duration session
Description WordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.

Cookie BCSessionID
Duration 1 year 1 month 4 days
Description Blueconic sets this cookie as a unique identifier for the BlueConic profile.

Cookie _octo
Duration 1 year
Description No description available.

Cookie logged_in
Duration 1 year
Description No description available.

Cookie __Secure-YEC
Duration past
Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video

Cookie __eoi
Duration 6 months
Description Description is currently not available.

Cookie AWSALBTGCORS
Duration 7 days
Description No description available.

Cookie login-status-p
Duration past
Description Description is currently not available.

Cookie AWSALBTG
Duration 7 days
Description No description available.

Cookie csrf_token
Duration session
Description No description available.

Cookie token_v2
Duration 1 day
Description Description is currently not available.

Cookie D
Duration 1 year
Description Description is currently not available.

Cookie PHPSESSID
Duration session
Description This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.

Cookie VISITOR_PRIVACY_METADATA
Duration 6 months
Description YouTube sets this cookie to store the user's cookie consent state for the current domain.

Cookie cookietest
Duration session
Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.

Cookie __Host-airtable-session
Duration 1 year
Description This cookie is used to enable us to integrate the services of Airtable.

Cookie __Host-airtable-session.sig
Duration 1 year
Description This cookie is used to enable us to integrate the services of Airtable.

Cookie m
Duration 1 year 1 month 4 days
Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.

Cookie BIGipServer*
Duration session
Description Marketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user.

Cookie __cfruid
Duration session
Description Cloudflare sets this cookie to identify trusted web traffic.

Cookie _GRECAPTCHA
Duration 6 months
Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.

Cookie __Secure-YNID
Duration 6 months
Description Google cookie used to protect user security and prevent fraud, especially during the login process.

Cookie cookieyes-consent
Duration 1 year
Description CookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.

Functional

[x]

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

Cookie lidc
Duration 1 day
Description LinkedIn sets the lidc cookie to facilitate data center selection.

Cookie brw
Duration 1 year
Description No description available.

Cookie brwConsent
Duration 5 minutes
Description Description is currently not available.

Cookie WMF-Uniq
Duration 1 year
Description Description is currently not available.

Cookie loom_anon_comment
Duration 1 year
Description No description available.

Cookie loom_referral_video
Duration session
Description Description is currently not available.

Cookie VISITOR_INFO1_LIVE
Duration 6 months
Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.

Cookie yt-remote-connected-devices
Duration Never Expires
Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos.

Cookie ytidb::LAST_RESULT_ENTRY_KEY
Duration Never Expires
Description The cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.

Cookie yt-remote-device-id
Duration Never Expires
Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos.

Cookie yt-remote-session-name
Duration session
Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.

Cookie yt-remote-fast-check-period
Duration session
Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.

Cookie yt-remote-session-app
Duration session
Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.

Cookie yt-remote-cast-available
Duration session
Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.

Cookie yt-remote-cast-installed
Duration session
Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.

Cookie cp_session
Duration 3 months
Description Codepen sets this cookie for Help systems found in the website.

Cookie loid
Duration 1 year 1 month 4 days
Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.

Analytics

[x]

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

Cookie __hstc
Duration 6 months
Description Hubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).

Cookie hubspotutk
Duration 6 months
Description HubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.

Cookie _ga
Duration 1 year 1 month 4 days
Description Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.

Cookie _ga_*
Duration 1 year 1 month 4 days
Description Google Analytics sets this cookie to store and count page views.

Cookie __Host-psifi.analyticsTrace
Duration 6 hours
Description Description is currently not available.

Cookie __Host-psifi.analyticsTraceV2
Duration 6 hours
Description Description is currently not available.

Cookie _gh_sess
Duration session
Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.

Cookie YSC
Duration session
Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.

Cookie ajs_anonymous_id
Duration 1 year
Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.

Cookie vuid
Duration 1 year 1 month 4 days
Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website.

Performance

[x]

Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.

Cookie AWSALB
Duration 7 days
Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.

Cookie acq
Duration past
Description Description is currently not available.

Cookie acq.sig
Duration past
Description Description is currently not available.

Cookie ptc
Duration 2 years
Description No description available.

[x]

Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.

Cookie muc_ads
Duration 1 year 1 month 4 days
Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website.

Cookie guest_id_marketing
Duration 1 year 1 month 4 days
Description Twitter sets this cookie to identify and track the website visitor.

Cookie guest_id_ads
Duration 1 year 1 month 4 days
Description Twitter sets this cookie to identify and track the website visitor.

Cookie personalization_id
Duration 1 year 1 month 4 days
Description Twitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.

Cookie guest_id
Duration 1 year 1 month 4 days
Description Twitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.

Cookie bcookie
Duration 1 year
Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.

Cookie __Secure-ROLLOUT_TOKEN
Duration 6 months
Description YouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.

Cookie yt.innertube::nextId
Duration Never Expires
Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.

Cookie yt.innertube::requests
Duration Never Expires
Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.

Cookie session_tracker
Duration session
Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.

Cookie edgebucket
Duration session
Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.

Cookie did
Duration 1 year
Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.

Uncategorised

Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet.

No cookies to display.

Reject All Save My Preferences Accept All

Skip to content

Publish AI, ML & data-science insights to a global community of data professionals.

Sign in

Submit an Article

* *

Write For TDS

Toggle Mobile Navigation

Toggle Search

Search

Data Science

They Requested It. I Built It. Nobody Ever Used It.

Why good data work gets ignored after delivery.

Hayden Kastens

May 27, 2026

6 min read

Share

Illustration by Alghozy on Unsplash

Stakeholders came to us asking for a model.

We built a proof of concept. Got the green light. Delivered the model.

Weeks of work…all to hear nothing.

It’s a tale as old as time, and one that plagues data professionals everywhere, from analysts to ML engineers.

So, what happened?

Your Model is a Mystery

Our profession is one rooted in modern computer science and technological advancements. Many of the most powerful solutions at our fingertips are ones that would have been too computationally expensive decades ago. With the reliance on the newest, most capable technical breakthroughs, comes skepticism.

In data science, we have the ability to create incredibly complex models. My team alone has hundreds of standard features in our feature library that we provide to each new model build. We tune dozens of hyperparameters and use powerful algorithms that iterate over hundreds of runs to maximize predictive performance. This process can create models with incredible accuracy, but it comes at a cost: explainability.

_There is a fine line between a strong model, and a black box that can’t even be explained by the ones who built it._

The explainability-accuracy tradeoff is a big factor in my industry, healthcare, in particular. Customers and stakeholders are often doctors and clinicians. These doctors are used to making clincial judgements using their years of expertise and in-depth knowledge of medicine. While a predictive model may be good at predicting a given outcome, if it cannot be explained well, clinicians will question its trustworthiness. If doctors have to choose between a trusted and proven clinical process, or a black box model with cryptic features and inexplainable algorithms, they will likely choose the clinical process every time.

So, what can you do to avoid this? I find the most success by providing customers with an easily digestible model brief. This is a set of slides that walks the customers through the model. It starts by defining the population of interest, the target, the features, and then ends with proof of concept performance and validation. Along the way, I am sure to define metrics in terms of the business question, putting myself in the customer’s shoes. I avoid pure stat-talk and keep definitions grounded in the customer’s goals. If the model is complex, I stick to high-level explanations of the algorithm and be sure to communicate why I chose such an extensive feature set (or such a simple one). Developing a comprehensive model brief is a crucial step in pulling back the curtain and allowing the customers to understand the model using terms they are familiar with.

Illustration by Irvan maulana on Unsplash

Your Solution Took Too Long

Building working models takes time. From the back and forth correspondence with customers, to unexpected twists you didn’t see coming, designing an effective, useful model is not a quick task. And then there’s deployment. That’s a whole process in and of itself.

What doesn’t wait patiently is the real world. Customers are living their day-to-day with the tools they already have at their disposal. The tools that existed before they came to you for help. If the model build takes too long, they could abandon the idea altogether, or find creative solutions that don’t involve predictive models.

We see this all the time in healthcare. Stakeholders will request a model. After a few roadblocks (stalled communication from requestors, data access issues, deployment bugs, etc.), weeks of development stretches into months. Finally, you are ready to present findings after everything is validated and working as expected. You attempt to set the meeting and get heartbreak: “We no longer need the model, we figured it out ourselves.” The hosptial setting is a fast-paced environment. Staff doesn’t have time to sit around waiting for months on end. They can and will come up with creative solutions to improve care for their patients, even if that means sacrificing the use of a shiny predictive model.

There is a saying I live by at work: “Don’t let the perfect get in the way of the good”. Build fast. Ideate, refine, review…but always be moving forward. Perfection can prevent you from providing valuable insights. The world moves quickly, and if you get stuck in the build phase for too long, the world will move on without you. So, push that v1. If you discover a better way of doing things later on, it can be first on your list of improvements for v2. Some solution is almost always better than no solution.

Illustration by VectorElements on Unsplash

If things are moving slower than planned, then you need to communicate with customers early and often. Keep them posted on your progress, and provide them with a sneak peak to keep them engaged and excited for the final product. Bide your time while you grind to get v1 working and into their hands.

Your Model Isn’t Easy to Consume

Building a good predictive model is only half the battle. In most industries, the stakeholders are busy. In heathcare, the doctors and nurses are absolutely swamped caring for patients. If the data science team comes to an on-floor care team to pitch their newest, most accurate model, but accessing the predictions adds complexity to their workflow and slows them down in the process, the model will never be used. The same can be seen in most industries. Stakeholders want solutions that can increase efficiency, performance, and productivity, not ones that only add complexity to their already busy days.

_If predictions introduce friction, you are forging a path towards abandonment, not adoption._

Serving predictions that are easy to consume can be one of the biggest challenges for data scientists. We may be skilled at creating precise and accurate models, but integrating the model into customers’ daily lives comes less naturally. This part is less about numbers, probabilities, and statistical acumen, and more about operations, business knowledge, and familiarity with the day-to-day processes of the requestors.

In the hospital setting, this looks like integration into Epic, the electronic health record software used system-wide. Instead of requiring busy clinicians to log into a separate system to see predictions, they can access them right there, in the patients’ charts, alongside their other clinical tools and patient data. In other industries, the same idea applies. Don’t disupt the current process. Fit into it.

Illustration by Luky Triohandoko on Unsplash

Wrapping Up

One of the biggest disappointments a data scientist can face throughout their career is their hard work going unused. It happens more than one would like to think, and it’s easy to blame the customer. After all, it’s easier on the ego.

In reality, there may be some crucial elements the data scientist neglected somewhere along the line of development. Being aware of the common pitfalls can help data scientists get their models across the finish line. The _real_ finish line: adoption.